{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T11:25:02Z","timestamp":1767007502843,"version":"3.37.3"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T00:00:00Z","timestamp":1601337600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T00:00:00Z","timestamp":1601337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Biomed Semant"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n<jats:title>Background<\/jats:title>\n<jats:p>Medical knowledge is accumulated in scientific research papers along time. In order to exploit this knowledge by automated systems, there is a growing interest in developing text mining methodologies to extract, structure, and analyze in the shortest time possible the knowledge encoded in the large volume of medical literature. In this paper, we use the Latent Dirichlet Allocation approach to analyze the correlation between funding efforts and actually published research results in order to provide the policy makers with a systematic and rigorous tool to assess the efficiency of funding programs in the medical area.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>We have tested our methodology in the Revista M\u00e9dica de Chile, years 2012-2015. 50 relevant semantic topics were identified within 643 medical scientific research papers. Relationships between the identified semantic topics were uncovered using visualization methods. We have also been able to analyze the funding patterns of scientific research underlying these publications. We found that only 29% of the publications declare funding sources, and we identified five topic clusters that concentrate 86% of the declared funds.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>Our methodology allows analyzing and interpreting the current state of medical research at a national level. The funding source analysis may be useful at the policy making level in order to assess the impact of actual funding policies, and to design new policies.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s13326-020-00226-w","type":"journal-article","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T09:03:08Z","timestamp":1601370188000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Methodologically grounded semantic analysis of large volume of chilean medical literature data applied to the analysis of medical research funding efficiency in Chile"],"prefix":"10.1186","volume":"11","author":[{"given":"Patricio","family":"Wolff","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebasti\u00e1n","family":"R\u00edos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Clavijo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7373-4097","authenticated-orcid":false,"given":"Manuel","family":"Gra\u00f1a","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miguel","family":"Carrasco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"issue":"3","key":"226_CR1","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/2041-1480-3-S3-S6","volume":"3","author":"S Kim","year":"2012","unstructured":"Kim S, Wilbur WJ. Thematic clustering of text documents using an em-based approach. J Biomed Semant. 2012; 3(3):6. https:\/\/doi.org\/10.1186\/2041-1480-3-S3-S6.","journal-title":"J Biomed Semant"},{"issue":"1","key":"226_CR2","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s13326-017-0150-0","volume":"8","author":"Y Papanikolaou","year":"2017","unstructured":"Papanikolaou Y, Tsoumakas G, Laliotis M, Markantonatos N, Vlahavas I. Large-scale online semantic indexing of biomedical articles via an ensemble of multi-label classification models. J Biomed Semant. 2017; 8(1):43. https:\/\/doi.org\/10.1186\/s13326-017-0150-0.","journal-title":"J Biomed Semant"},{"issue":"1","key":"226_CR3","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s13326-015-0019-z","volume":"6","author":"N Collier","year":"2015","unstructured":"Collier N, Oellrich A, Groza T. Concept selection for phenotypes and diseases using learn to rank. J Biomed Semant. 2015; 6(1):24. https:\/\/doi.org\/10.1186\/s13326-015-0019-z.","journal-title":"J Biomed Semant"},{"issue":"1","key":"226_CR4","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s13326-018-0181-1","volume":"9","author":"M Arguello Casteleiro","year":"2018","unstructured":"Arguello Casteleiro M, Demetriou G, Read W, Fernandez Prieto MJ, Maroto N, Maseda Fernandez D, Nenadic G, Klein J, Keane J, Stevens R. Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature. J Biomed Semant. 2018; 9(1):13. https:\/\/doi.org\/10.1186\/s13326-018-0181-1.","journal-title":"J Biomed Semant"},{"issue":"1","key":"226_CR5","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s13326-015-0021-5","volume":"6","author":"D Weissenborn","year":"2015","unstructured":"Weissenborn D, Schroeder M, Tsatsaronis G. Discovering relations between indirectly connected biomedical concepts. J Biomed Semant. 2015; 6(1):28. https:\/\/doi.org\/10.1186\/s13326-015-0021-5.","journal-title":"J Biomed Semant"},{"issue":"11","key":"226_CR6","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/1471-2105-15-S11-S11","volume":"15","author":"W Zhao","year":"2014","unstructured":"Zhao W, Zou W, Chen JJ. Topic modeling for cluster analysis of large biological and medical datasets. BMC Bioinformatics. 2014; 15(11):11. https:\/\/doi.org\/10.1186\/1471-2105-15-S11-S11.","journal-title":"BMC Bioinformatics"},{"key":"226_CR7","volume-title":"23rd International Conference of the European Federation for Medical Informatics","author":"X Wu","year":"2011","unstructured":"Wu X, Guo H, Cai K, Zhang L, Su Z. Linkthemall mining hybrid semantic associations from medical publications. In: 23rd International Conference of the European Federation for Medical Informatics. Oslo: University of Oslo: 2011."},{"key":"226_CR8","first-page":"43","volume":"2014","author":"DC Li","year":"2014","unstructured":"Li DC, Thermeau T, Chute C, Liu H. Discovering associations among diagnosis groups using topic modeling. AMIA Jt Summits Transl Sci Proc. 2014; 2014:43\u201349.","journal-title":"AMIA Jt Summits Transl Sci Proc"},{"key":"226_CR9","first-page":"132","volume":"2010","author":"SP Crain","year":"2010","unstructured":"Crain SP, Yang S-H, Zha H, Jiao Y. Dialect topic modeling for improved consumer medical search. AMIA Annu Symp Proc. 2010; 2010:132\u20136.","journal-title":"AMIA Annu Symp Proc"},{"issue":"6","key":"226_CR10","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1089\/106652703322756104","volume":"10","author":"H Shatkay","year":"2003","unstructured":"Shatkay H, Feldman R. Mining the biomedical literature in the genomic era: An overview. J Comput Biol. 2003; 10(6):821\u201355. https:\/\/doi.org\/10.1089\/106652703322756104. PMID: 14980013.","journal-title":"J Comput Biol"},{"issue":"3","key":"226_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0017243","volume":"6","author":"H Wang","year":"2011","unstructured":"Wang H, Ding Y, Tang J, Dong X, He B, Qiu J, Wild DJ. Finding complex biological relationships in recent pubmed articles using bio-lda. PLOS ONE. 2011; 6(3):1\u201314. https:\/\/doi.org\/10.1371\/journal.pone.0017243.","journal-title":"PLOS ONE"},{"key":"226_CR12","volume-title":"AI 2009: Advances in Artificial Intelligence","author":"D Newman","year":"2009","unstructured":"Newman D, Karimi S, Cavedon L. Using topic models to interpret medline\u2019s medical subject headings In: Nicholson A, Li X, editors. AI 2009: Advances in Artificial Intelligence. Berlin, Heidelberg: Springer: 2009. p. 270\u20139."},{"key":"226_CR13","volume-title":"Biocomputing 2012","author":"Y Wu","year":"2012","unstructured":"Wu Y, Liu M, Zheng WJ, Zhao Z, Xu H. Ranking gene-drug relationships in biomedical literature using latent dirichlet allocation. In: Biocomputing 2012. Singapore: World Scientific: 2012. p. 422\u201333."},{"issue":"suppl 1","key":"226_CR14","doi-asserted-by":"publisher","first-page":"5228","DOI":"10.1073\/pnas.0307752101","volume":"101","author":"TL Griffiths","year":"2004","unstructured":"Griffiths TL, Steyvers M. Finding scientific topics. Proc Natl Acad Sci. 2004; 101(suppl 1):5228\u201335. https:\/\/doi.org\/10.1073\/pnas.0307752101.","journal-title":"Proc Natl Acad Sci"},{"key":"226_CR15","volume-title":"IMMM 2013 The Third International Conference on Advances in Information Mining and Management","author":"LI Barbosa-santill","year":"2013","unstructured":"Barbosa-santill LI. Analysis of medical publications with latent semantic analysis method. In: IMMM 2013 The Third International Conference on Advances in Information Mining and Management. Lisbon: International Academy Research and Industry Association: 2013. p. 81\u201386."},{"key":"226_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2139\/ssrn.2096159","volume":"78","author":"T Magerman","year":"2011","unstructured":"Magerman T, Looy BV, Baesens B, Debackere K. Assessment of latent semantic analysis (LSA) text mining algorithms for large scale mapping of patent and scientific publication documents. SSRN. 2011; 78:1\u201378. https:\/\/doi.org\/10.2139\/ssrn.2096159.","journal-title":"SSRN"},{"key":"226_CR17","first-page":"719","volume":"130","author":"AG Goic","year":"2002","unstructured":"Goic AG. La Revista M\u00e9tdica de Chile y la educaci\u00f3n en medicina. Rev Med Chile. 2002; 130:719\u201322.","journal-title":"Rev Med Chile"},{"issue":"5","key":"226_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v025.i05","volume":"25","author":"I Feinerer","year":"2008","unstructured":"Feinerer I, Hornik K, Meyer D. Text mining infrastructure in r. J Stat Softw. 2008; 25(5):1\u201354.","journal-title":"J Stat Softw"},{"key":"226_CR19","unstructured":"Feinerer I, Hornik K. Tm: Text Mining Package. R package version 0.7-7. 2019. https:\/\/CRAN.R-project.org\/package=tm. Accessed 1 Sept 2020."},{"issue":"13","key":"226_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v040.i13","volume":"40","author":"B Gr\u00fcn","year":"2011","unstructured":"Gr\u00fcn B, Hornik K. Topicmodels: An r package for fitting topic models. J Stat Softw Artic. 2011; 40(13):1\u201330. https:\/\/doi.org\/10.18637\/jss.v040.i13.","journal-title":"J Stat Softw Artic"},{"issue":"7","key":"226_CR21","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1016\/j.neucom.2008.06.011","volume":"72","author":"J Cao","year":"2009","unstructured":"Cao J, Xia T, Li J, Zhang Y, Tang S. A density-based method for adaptive LDA model selection. Neurocomputing. 2009; 72(7):1775\u201381.","journal-title":"Neurocomputing"},{"key":"226_CR22","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"R Arun","year":"2010","unstructured":"Arun R, Suresh V, Veni Madhavan CE, Narasimha Murthy MN. On finding the natural number of topics with latent dirichlet allocation: Some observations In: Zaki MJ, Yu JX, Ravindran B, Pudi V, editors. Advances in Knowledge Discovery and Data Mining. Berlin, Heidelberg: Springer: 2010. p. 391\u2013402."},{"key":"226_CR23","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-3110","volume-title":"Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces","author":"C Sievert","year":"2014","unstructured":"Sievert C, Shirley K. LDAvis: A method for visualizing and interpreting topics. In: Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces. Baltimore, Maryland, USA: Association for Computational Linguistics: 2014. p. 63\u201370. https:\/\/doi.org\/10.3115\/v1\/W14-3110https:\/\/www.aclweb.org\/anthology\/W14-3110."},{"issue":"4","key":"226_CR24","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1145\/2133806.2133826","volume":"55","author":"DM Blei","year":"2012","unstructured":"Blei DM. Probabilistic topic models. Commun ACM. 2012; 55(4):77\u201384. https:\/\/doi.org\/10.1145\/2133806.2133826.","journal-title":"Commun ACM"},{"issue":"11","key":"226_CR25","doi-asserted-by":"publisher","first-page":"15169","DOI":"10.1007\/s11042-018-6894-4","volume":"78","author":"H Jelodar","year":"2019","unstructured":"Jelodar H, Wang Y, Yuan C, Feng X, Jiang X, Li Y, Zhao L. Latent dirichlet allocation (lda) and topic modeling: models, applications, a survey. Multimed Tools Appl. 2019; 78(11):15169\u2013211. https:\/\/doi.org\/10.1007\/s11042-018-6894-4.","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"226_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3094786","volume":"9","author":"Y Gao","year":"2017","unstructured":"Gao Y, Li Y, Lau RYK, Xu Y, Bashar MA. Finding semantically valid and relevant topics by association-based topic selection model. ACM Trans Intell Syst Technol. 2017; 9(1):1\u201322. https:\/\/doi.org\/10.1145\/3094786.","journal-title":"ACM Trans Intell Syst Technol"},{"key":"226_CR27","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res. 2003; 3:993\u20131022.","journal-title":"J Mach Learn Res"},{"key":"226_CR28","volume-title":"Advanced Visual Interfaces","author":"J Chuang","year":"2012","unstructured":"Chuang J, Manning CD, Heer J. Termite: visualization techniques for assessing textual topic models. In: Advanced Visual Interfaces. New York: ACM Press: 2012. http:\/\/vis.stanford.edu\/papers\/termite."},{"key":"226_CR29","doi-asserted-by":"crossref","unstructured":"Graham S, Weingart S, Milligan I. Getting started with topic modeling and mallet. Programm Historian. 2012; 1. https:\/\/programminghistorian.org\/en\/lessons\/topic-modeling-and-mallet.","DOI":"10.46430\/phen0017"},{"key":"226_CR30","doi-asserted-by":"publisher","DOI":"10.1109\/INFVIS.2000.885098","volume-title":"IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings","author":"S Havre","year":"2000","unstructured":"Havre S, Hetzler B, Nowell L. Themeriver: Visualizing theme changes over time. In: IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings. Pscataway: IEEE: 2000. p. 115\u2013123. https:\/\/doi.org\/10.1109\/INFVIS.2000.885098."},{"issue":"6","key":"226_CR31","doi-asserted-by":"publisher","first-page":"1172","DOI":"10.1109\/TVCG.2010.154","volume":"16","author":"N Cao","year":"2010","unstructured":"Cao N, Sun J, Lin Y, Gotz D, Liu S, Qu H. Facetatlas: Multifaceted visualization for rich text corpora. IEEE Trans Vis Comput Graphic. 2010; 16(6):1172\u201381. https:\/\/doi.org\/10.1109\/TVCG.2010.154.","journal-title":"IEEE Trans Vis Comput Graphic"},{"key":"226_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-71265-9","volume-title":"Introduction to Applied Bayesian Statistics and Estimation for Social Scientists","author":"S Lynch","year":"2007","unstructured":"Lynch S. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York: Springer; 2007. https:\/\/doi.org\/10.1007\/978-0-387-71265-9."},{"issue":"1","key":"226_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BF02289565","volume":"29","author":"JB Kruskal","year":"1964","unstructured":"Kruskal JB. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika. 1964; 29(1):1\u201327. https:\/\/doi.org\/10.1007\/BF02289565.","journal-title":"Psychometrika"},{"issue":"2","key":"226_CR34","first-page":"355","volume":"82","author":"HD Shapiro","year":"1991","unstructured":"Shapiro HD, Petroski H. Reviewed work: The pencil: A history of design and circumstance by henry petroski. JSTOR. 1991; 82(2):355\u201356.","journal-title":"JSTOR"}],"container-title":["Journal of Biomedical Semantics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13326-020-00226-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13326-020-00226-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13326-020-00226-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T00:05:49Z","timestamp":1632873949000},"score":1,"resource":{"primary":{"URL":"https:\/\/jbiomedsem.biomedcentral.com\/articles\/10.1186\/s13326-020-00226-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,29]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["226"],"URL":"https:\/\/doi.org\/10.1186\/s13326-020-00226-w","relation":{},"ISSN":["2041-1480"],"issn-type":[{"type":"electronic","value":"2041-1480"}],"subject":[],"published":{"date-parts":[[2020,9,29]]},"assertion":[{"value":"14 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Does not apply.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"12"}}